Social/Sexual Networks of People Newly Diagnosed with HIV in Ibadan, Nigeria.

Young men who have sex with men (YMSM) in Nigeria are ten times more likely to be living with HIV-1 than other young men. Due to stigma and criminalization of same-sex sexual behavior, YMSM sexual networks are likely to overlap with those of the general population, leading to a generalized HIV-1 epidemic. Due to limited research on social/sexual network dynamics related to HIV-1 in Nigeria, our study focused on YMSM and sought to assess the feasibility and acceptability of collecting social and sexual network data in Network Canvas from individuals newly diagnosed with HIV-1 in Ibadan, Nigeria. The Network Canvas software was piloted at three sites in Ibadan, Nigeria to collect social/sexual network data from 151 individuals newly diagnosed with HIV-1. Our study sample included 37.7% YMSM; participants reported a mean of 2.6 social alters and 2.6 sexual alters. From the 151 egos and 634 alters, 85 potential unique individuals (194 total) were identified; 65 egos/alters were collapsed into 25 unique individuals. Our success collecting network data from individuals newly diagnosed with HIV-1 in Ibadan demonstrates clear feasibility and acceptability of the approach and the use of Network Canvas to capture and manage these data.

Changes in Sexual Behaviors Due to Mpox: a Cross-Sectional Study of Sexual and Gender Minority Individuals in Illinois.

The spread of the monkeypox virus (mpox) in 2022 primarily within the sexual networks of men who have sex with men (MSM) triggered a potentially stigmatizing public health response in the USA. Despite mpox being primarily spread through skin-to-skin contact, most messaging has promoted abstinence and/or reduction in sexual risk behaviors. More research is needed on decreases in sexual risk behaviors among sexual and gender minority (SGM) youth and young adults (YYA) related to the most recent mpox epidemic and whether there are factors associated with these decreases in sexual risk behavior. Participants within an ongoing cohort study of SGM YYA who reside in Illinois were offered the opportunity to participate in an mpox survey between September 10th and September 20th, 2022. Analyses looked at demographic factors associated with sexual activity since the start of the outbreak, as well as associations with two sexual risk reduction factors. Survey participation was 68.7% (322/469). Three-quarters of participants (82.6%) reported sexual activity since June 1st. Most sexually active participants (83.5%) adopted at least one sexual risk reduction behavior due to mpox. Black and Latinx individuals were less likely to be sexually active but more likely to report risk reduction behaviors (31.3% and 22.6%, respectively). Participants who received the mpox vaccine were more likely to report sexual activity. SGM YYA in Illinois reported that their sexual behaviors were impacted by the mpox outbreak. However, associations between vaccination and sexual behavior demonstrate that those who are vaccinated do adopt protective methods despite not decreasing sexual activity. Therefore, sex-positive communications and harm reduction messaging may be more appropriate as opposed to abstinence-only prevention, which can further stigmatize an already marginalized group.

Efficacy of the spatial repellent product Mosquito Shield™ against wild pyrethroid-resistant Anopheles arabiensis in south-eastern Tanzania.

BACKGROUND: Spatial repellents that create airborne concentrations of an active ingredient (AI) within a space offer a scalable solution to further reduce transmission of malaria, by disrupting mosquito behaviours in ways that ultimately lead to reduced human-vector contact. Passive emanator spatial repellents can protect multiple people within the treated space and can last for multiple weeks without the need for daily user touchpoints, making them less intrusive interventions. They may be particularly advantageous in certain use cases where implementation of core tools may be constrained, such as in humanitarian emergencies and among mobile at-risk populations. The purpose of this study was to assess the efficacy of Mosquito Shield™ deployed in experimental huts against wild, free-flying, pyrethroid-resistant Anopheles arabiensis mosquitoes in Tanzania over 1 month. METHODS: The efficacy of Mosquito Shield™ transfluthrin spatial repellent in reducing mosquito lands and blood-feeding was evaluated using 24 huts: sixteen huts were allocated to Human Landing Catch (HLC) collections and eight huts to estimating blood-feeding. In both experiments, half of the huts received no intervention (control) while the remaining received the intervention randomly allocated to huts and remained fixed for the study duration. Outcomes measured were mosquito landings, blood-fed, resting and dead mosquitoes. Data were analysed by multilevel mixed effects regression with appropriate dispersion and link function accounting for volunteer, hut and day. RESULTS: Landing inhibition was estimated to be 70% (57-78%) [IRR 0.30 (95% CI 0.22-0.43); p < 0.0001] and blood-feeding inhibition was estimated to be 69% (56-79%) [IRR 0.31 (95% CI 0.21-0.44; p < 0.0001] There was no difference in the protective efficacy estimates of landing and blood-feeding inhibition [IRR 0.98 (95% CI 0.53-1.82; p = 0.958]. CONCLUSIONS: This study demonstrated that Mosquito Shield™ was efficacious against a wild pyrethroid-resistant strain of An. arabiensis mosquitoes in Tanzania for up to 1 month and could be used as a complementary or stand-alone tool where gaps in protection offered by core malaria vector control tools exist. HLC is a suitable technique for estimating bite reductions conferred by spatial repellents especially where direct blood-feeding measurements are not practical or are ethically limited.

Predictors of Mpox vaccine uptake among sexual and gender minority young adults living in Illinois: Unvaccinated vs. double vs. single dose vaccine recipients.

INTRODUCTION: The 2022 global outbreak of Monkeypox virus (Mpox), which has primarily spread through the sexual networks of sexual and gender minority (SGM) individuals, has introduced new public health challenges. While an efficacious Mpox vaccine is in active circulation, few Mpox vaccine studies have examined its uptake among SGM groups. The aims of this study were to investigate (a) the prevalence of Mpox vaccine uptake among SGM and (b) the contextual, Mpox-disease specific, and Mpox-vaccine specific factors associated with Mpox vaccine among SGM. METHODS: We conducted a cross-sectional survey in Illinois, USA in September 2022; 320 young SGM completed self-administered questionnaires. Multinomial logistic regression was used to assess the contextual, Mpox-disease specific, and Mpox-vaccine specific factors associated with Mpox vaccine uptake. Adjusted Odds Ratios (aORs) and 95 % Confidence Intervals (CI) are reported. RESULTS: Approximately 50 % of the SGM participants included in this study had received at least their first dose of the Mpox vaccine. Multinomial regression analysis showed that individuals who had recently experienced food insecurity, had higher degrees of fear of social rejection due to Mpox acquisition, and were more Mpox-vaccine hesitant were more likely to be unvaccinated. Conversely, knowing people who have contracted Mpox, having higher formal educational attainment, having higher degrees of Mpox-related internalized heterosexism, and being more concerned about one's safety regarding Mpox morbidity were more likely to be double-dosers. CONCLUSION: Approximately 50 % of the SGMs included in this study received at least their first dose of the Mpox vaccine; however, only one-quarter of participants completed the recommended 2-dose Mpox regimen. Our findings indicate that socioeconomic stability, fear of social rejection due to disease acquisition, and Mpox-specific vaccine hesitancy may be important structural targets to consider when developing vaccine-uptake prevention and intervention strategies tailored to the needs of sexual and gender minorities.

Automatic segmentation of prostate cancer metastases in PSMA PET/CT images using deep neural networks with weighted batch-wise dice loss.

PURPOSE: Automatic and accurate segmentation of lesions in images of metastatic castration-resistant prostate cancer has the potential to enable personalized radiopharmaceutical therapy and advanced treatment response monitoring. The aim of this study is to develop a convolutional neural networks-based framework for fully-automated detection and segmentation of metastatic prostate cancer lesions in whole-body PET/CT images. METHODS: 525 whole-body PET/CT images of patients with metastatic prostate cancer were available for the study, acquired with the [18F]DCFPyL radiotracer that targets prostate-specific membrane antigen (PSMA). U-Net (1)-based convolutional neural networks (CNNs) were trained to identify lesions on paired axial PET/CT slices. Baseline models were trained using batch-wise dice loss, as well as the proposed weighted batch-wise dice loss (wDice), and the lesion detection performance was quantified, with a particular emphasis on lesion size, intensity, and location. We used 418 images for model training, 30 for model validation, and 77 for model testing. In addition, we allowed our model to take n = 0,2, …, 12 neighboring axial slices to examine how incorporating greater amounts of 3D context influences model performance. We selected the optimal number of neighboring axial slices that maximized the detection rate on the 30 validation images, and trained five neural networks with different architectures. RESULTS: Model performance was evaluated using the detection rate, Dice similarity coefficient (DSC) and sensitivity. We found that the proposed wDice loss significantly improved the lesion detection rate, lesion-wise DSC and lesion-wise sensitivity compared to the baseline, with corresponding average increases of 0.07 (p-value = 0.01), 0.03 (p-value = 0.01) and 0.04 (p-value = 0.01), respectively. The inclusion of the first two neighboring axial slices in the input likewise increased the detection rate by 0.17, lesion-wise DSC by 0.05, and lesion-wise mean sensitivity by 0.16. However, there was a minimal effect from including more distant neighboring slices. We ultimately chose to use a number of neighboring slices equal to 2 and the wDice loss function to train our final model. To evaluate the model's performance, we trained three models using identical hyperparameters on three different data splits. The results showed that, on average, the model was able to detect 80% of all testing lesions, with a detection rate of 93% for lesions with maximum standardized uptake values (SUVmax) greater than 5.0. In addition, the average median lesion-wise DSC was 0.51 and 0.60 for all the lesions and lesions with SUVmax>5.0, respectively, on the testing set. Four additional neural networks with different architectures were trained, and they both yielded stronger performance of segmenting lesions whose SUVmax>5.0 compared to the rest of lesions. CONCLUSION: Our results demonstrate that prostate cancer metastases in PSMA PET/CT images can be detected and segmented using CNNs. The segmentation performance strongly depends on the intensity, size, and the location of lesions, and can be improved by using specialized loss functions. Specifically, the models performed best in detection of lesions with SUVmax>5.0. Another challenge was to accurately segment lesions close to the bladder. Future work will focus on improving the detection of lesions with lower SUV values by designing custom loss functions that take into account the lesion intensity, using additional data augmentation techniques, and reducing the number of false lesions by developing methods to better separate signal from noise.

Diagnosis and management of basal cell carcinoma: interdisciplinary consensus statements of Jose R. Reyes Memorial Medical Center, a Department of Health Tertiary Hospital in Manila, Philippines.

BACKGROUND: Basal cell carcinoma (BCC) is the most common cutaneous malignancy. Multiple risk factors are associated in the development of BCC, with ultraviolet light and genetics playing major roles. AIMS: The departments of dermatology, medical oncology, ophthalmology, otorhinolaryngology, head and neck surgery, plastic surgery, and radiation oncology of the Jose R. Reyes Memorial Medical Center, Manila, Philippines, have convened and formulated consensus statements on the diagnosis and management of BCC patients seen in the institution. CONCLUSION: The summary of the recommendations is: (1) Surgery is the treatment of choice for BCC. The range of margins (2-4 mm) depends on the type of BCC. (2) Mohs micrographic surgery (MMS) is indicated for high risk BCC. (3) Topical treatment with imiquimod or 5-flourouracil (5-FU) may be used for superficial BCC. (4) Destructive methods (cryotherapy, curettage and electrodessication, photodynamic therapy) may be used for low risk BCC. (5) Medical and/or radiation therapy is advised for cases where surgery is contraindicated or tumor is not amenable to surgery. Metastasis of this malignancy is rare. Follow-up, which may continue up until 2 years, is recommended for high risk BCC.

Are Illinois Contraceptive Providers Comfortable Providing Care to Adolescents? Results From a Statewide Provider Needs Assessment.

OBJECTIVES: Legislation allows adolescents to access comprehensive contraceptive care; however, provider practices remain unclear. We examined predictors of provider knowledge and comfort surrounding the provision of contraceptive care to adolescents. METHODS: We mailed a survey to Illinois contraceptive providers (n = 251). Study outcomes include 1) knowledge of adolescent consent laws, 2) comfort asking for time alone with adolescents, 3) comfort providing contraception to adolescents without parental consent, and 4) comfort providing long-acting reversible contraception (LARC) to adolescents without parental consent. Using multivariable logistic regression, we estimated adjusted odds ratios (aORs) and 95% confidence intervals (CIs). RESULTS: Most providers are knowledgeable of consent laws (90%) and report being comfortable asking for time alone with adolescents (94%) and comfortable providing contraception to adolescents without parental consent (88%). Having a large proportion of patients who are eligible for family planning services was associated with increased comfort asking for time alone with adolescents (aOR, 7.03; 95% CI, 1.58-31.3) and providing contraception to adolescents (aOR, 4.0; 95% CI, 1.4-11.1). Only one-half (54%) were comfortable providing LARC methods to adolescents, with higher comfort among providers who: received more than 2 days of formal family planning training (aOR, 2.77; 95% CI, 1.2-6.2), specialized in obstetrics-gynecology (aOR, 5.64; 95% CI, 2.1-15.1), and had a patient population with more than 50% patients from minoritized racial/ethnic groups (aOR, 2.9; 95% CI, 1.2-6.6). CONCLUSIONS: Although knowledge of consent laws was high, gaps remain. Only one-half of our sample indicated comfort with the provision of LARC methods without parental consent. Additional efforts to increase provider comfort with all contraceptive methods and training on adolescent-centered practices may be required to meet the needs of adolescent patients.

Deep learning models for COVID-19 chest x-ray classification: Preventing shortcut learning using feature disentanglement.

In response to the COVID-19 global pandemic, recent research has proposed creating deep learning based models that use chest radiographs (CXRs) in a variety of clinical tasks to help manage the crisis. However, the size of existing datasets of CXRs from COVID-19+ patients are relatively small, and researchers often pool CXR data from multiple sources, for example, using different x-ray machines in various patient populations under different clinical scenarios. Deep learning models trained on such datasets have been shown to overfit to erroneous features instead of learning pulmonary characteristics in a phenomenon known as shortcut learning. We propose adding feature disentanglement to the training process. This technique forces the models to identify pulmonary features from the images and penalizes them for learning features that can discriminate between the original datasets that the images come from. We find that models trained in this way indeed have better generalization performance on unseen data; in the best case we found that it improved AUC by 0.13 on held out data. We further find that this outperforms masking out non-lung parts of the CXRs and performing histogram equalization, both of which are recently proposed methods for removing biases in CXR datasets.

An Artificial Intelligence Dataset for Solar Energy Locations in India.

Rapid development of renewable energy sources, particularly solar photovoltaics (PV), is critical to mitigate climate change. As a result, India has set ambitious goals to install 500 gigawatts of solar energy capacity by 2030. Given the large footprint projected to meet renewables energy targets, the potential for land use conflicts over environmental values is high. To expedite development of solar energy, land use planners will need access to up-to-date and accurate geo-spatial information of PV infrastructure. In this work, we developed a spatially explicit machine learning model to map utility-scale solar projects across India using freely available satellite imagery with a mean accuracy of 92%. Our model predictions were validated by human experts to obtain a dataset of 1363 solar PV farms. Using this dataset, we measure the solar footprint across India and quantified the degree of landcover modification associated with the development of PV infrastructure. Our analysis indicates that over 74% of solar development In India was built on landcover types that have natural ecosystem preservation, or agricultural value.

Testing the Ability of Convolutional Neural Networks to Learn Radiomic Features.

BACKGROUND AND OBJECTIVE: Radiomics and deep learning have emerged as two distinct approaches to medical image analysis. However, their relative expressive power remains largely unknown. Theoretically, hand-crafted radiomic features represent a mere subset of features that neural networks can approximate, thus making deep learning a more powerful approach. On the other hand, automated learning of hand-crafted features may require a prohibitively large number of training samples. Here we directly test the ability of convolutional neural networks (CNNs) to learn and predict the intensity, shape, and texture properties of tumors as defined by standardized radiomic features. METHODS: Conventional 2D and 3D CNN architectures with an increasing number of convolutional layers were trained to predict the values of 16 standardized radiomic features from real and synthetic PET images of tumors, and tested. In addition, several ImageNet-pretrained advanced networks were tested. A total of 4000 images were used for training, 500 for validation, and 500 for testing. RESULTS: Features quantifying size and intensity were predicted with high accuracy, while shape irregularity and heterogeneity features had very high prediction errors and generalized poorly. For example, mean normalized prediction error of tumor diameter with a 5-layer CNN was 4.23 ± 0.25, while the error for tumor sphericity was 15.64 ± 0.93. We additionally found that learning shape features required an order of magnitude more samples compared to intensity and size features. CONCLUSIONS: Our findings imply that CNNs trained to perform various image-based clinical tasks may generally under-utilize the shape and texture information that is more easily captured by radiomics. We speculate that to improve the CNN performance, shape and texture features can be computed explicitly and added as auxiliary variables to the networks, or supplied as synthetic inputs.

Effective deep learning approaches for predicting COVID-19 outcomes from chest computed tomography volumes.

The rapid evolution of the novel coronavirus disease (COVID-19) pandemic has resulted in an urgent need for effective clinical tools to reduce transmission and manage severe illness. Numerous teams are quickly developing artificial intelligence approaches to these problems, including using deep learning to predict COVID-19 diagnosis and prognosis from chest computed tomography (CT) imaging data. In this work, we assess the value of aggregated chest CT data for COVID-19 prognosis compared to clinical metadata alone. We develop a novel patient-level algorithm to aggregate the chest CT volume into a 2D representation that can be easily integrated with clinical metadata to distinguish COVID-19 pneumonia from chest CT volumes from healthy participants and participants with other viral pneumonia. Furthermore, we present a multitask model for joint segmentation of different classes of pulmonary lesions present in COVID-19 infected lungs that can outperform individual segmentation models for each task. We directly compare this multitask segmentation approach to combining feature-agnostic volumetric CT classification feature maps with clinical metadata for predicting mortality. We show that the combination of features derived from the chest CT volumes improve the AUC performance to 0.80 from the 0.52 obtained by using patients' clinical data alone. These approaches enable the automated extraction of clinically relevant features from chest CT volumes for risk stratification of COVID-19 patients.

Positive interactions are common among culturable bacteria.

Interspecies interactions shape the structure and function of microbial communities. In particular, positive, growth-promoting interactions can substantially affect the diversity and productivity of natural and engineered communities. However, the prevalence of positive interactions and the conditions in which they occur are not well understood. To address this knowledge gap, we used kChip, an ultrahigh-throughput coculture platform, to measure 180,408 interactions among 20 soil bacteria across 40 carbon environments. We find that positive interactions, often described to be rare, occur commonly and primarily as parasitisms between strains that differ in their carbon consumption profiles. Notably, nongrowing strains are almost always promoted by strongly growing strains (85%), suggesting a simple positive interaction­mediated approach for cultivation, microbiome engineering, and microbial consortium design.

Interspecies bacterial competition regulates community assembly in the C. elegans intestine.

From insects to mammals, a large variety of animals hold in their intestines complex bacterial communities that play an important role in health and disease. To further our understanding of how intestinal bacterial communities assemble and function, we study the C. elegans microbiota with a bottom-up approach by feeding this nematode with bacterial monocultures as well as mixtures of two to eight bacterial species. We find that bacteria colonizing well in monoculture do not always do well in co-cultures due to interspecies bacterial interactions. Moreover, as community diversity increases, the ability to colonize the worm gut in monoculture becomes less important than interspecies interactions for determining community assembly. To explore the role of host-microbe adaptation, we compare bacteria isolated from C. elegans intestines and non-native isolates, and we find that the success of colonization is determined more by a species' taxonomy than by the isolation source. Lastly, by comparing the assembled microbiotas in two C. elegans mutants, we find that innate immunity via the p38 MAPK pathway decreases bacterial abundances yet has little influence on microbiota composition. These results highlight that bacterial interspecies interactions, more so than host-microbe adaptation or gut environmental filtering, play a dominant role in the assembly of the C. elegans microbiota.

Deep learning models for COVID-19 chest x-ray classification: Preventing shortcut learning using feature disentanglement.

In response to the COVID-19 global pandemic, recent research has proposed creating deep learning based models that use chest radiographs (CXRs) in a variety of clinical tasks to help manage the crisis. However, the size of existing datasets of CXRs from COVID-19+ patients are relatively small, and researchers often pool CXR data from multiple sources, for example, using different x-ray machines in various patient populations under different clinical scenarios. Deep learning models trained on such datasets have been shown to overfit to erroneous features instead of learning pulmonary characteristics -- a phenomenon known as shortcut learning. We propose adding feature disentanglement to the training process, forcing the models to identify pulmonary features from the images while penalizing them for learning features that can discriminate between the original datasets that the images come from. We find that models trained in this way indeed have better generalization performance on unseen data; in the best case we found that it improved AUC by 0.13 on held out data. We further find that this outperforms masking out non-lung parts of the CXRs and performing histogram equalization, both of which are recently proposed methods for removing biases in CXR datasets.

The Therapeutic Effectiveness of Full Spectrum Hemp Oil Using a Chronic Neuropathic Pain Model.

BACKGROUND: Few models exist that can control for placebo and expectancy effects commonly observed in clinical trials measuring 'Cannabis' pharmacodynamics. We used the Foramen Rotundum Inflammatory Constriction Trigeminal Infraorbital Nerve injury (FRICT-ION) model to measure the effect of "full-spectrum" whole plant extracted hemp oil on chronic neuropathic pain sensitivity in mice. METHODS: Male BALBc mice were submitted to the FRICT-ION chronic neuropathic pain model with oral insertion through an incision in the buccal/cheek crease of 3 mm of chromic gut suture (4-0). The suture, wedged along the V2 trigeminal nerve branch, creates a continuous irritation that develops into secondary mechanical hypersensitivity on the snout. Von Frey filament stimuli on the mouse whisker pad was used to assess the mechanical pain threshold from 0-6 h following dosing among animals (n = 6) exposed to 5 µL of whole plant extracted hemp oil combined with a peanut butter vehicle (0.138 mg/kg), the vehicle alone (n = 3) 7 weeks post-surgery, or a naïve control condition (n = 3). RESULTS: Mechanical allodynia was alleviated within 1 h (d = 2.50, p < 0.001) with a peak reversal effect at 4 h (d = 7.21, p < 0.001) and remained significant throughout the 6 h observation window. There was no threshold change on contralateral whisker pad after hemp oil administration, demonstrating the localization of anesthetic response to affected areas. CONCLUSION: Future research should focus on how whole plant extracted hemp oil affects multi-sensory and cognitive-attentional systems that process pain.

Massively parallel screening of synthetic microbial communities.

Microbial communities have numerous potential applications in biotechnology, agriculture, and medicine. Nevertheless, the limited accuracy with which we can predict interspecies interactions and environmental dependencies hinders efforts to rationally engineer beneficial consortia. Empirical screening is a complementary approach wherein synthetic communities are combinatorially constructed and assayed in high throughput. However, assembling many combinations of microbes is logistically complex and difficult to achieve on a timescale commensurate with microbial growth. Here, we introduce the kChip, a droplets-based platform that performs rapid, massively parallel, bottom-up construction and screening of synthetic microbial communities. We first show that the kChip enables phenotypic characterization of microbes across environmental conditions. Next, in a screen of ∼100,000 multispecies communities comprising up to 19 soil isolates, we identified sets that promote the growth of the model plant symbiont Herbaspirillum frisingense in a manner robust to carbon source variation and the presence of additional species. Broadly, kChip screening can identify multispecies consortia possessing any optically assayable function, including facilitation of biocontrol agents, suppression of pathogens, degradation of recalcitrant substrates, and robustness of these functions to perturbation, with many applications across basic and applied microbial ecology.